QuantifyNow - Outlearn your competition

QuantifyNow is a free, fully automated statistics and experimentation engine for marketing teams that provides rock-solid, actionable learnings from your existing marketing data.

With QuantifyNow, you can A/B/n test your media strategies, partners, targeting, ads, creatives, product ideas, and any other aspect of your marketing or campaigns instantly, without the need to involve data scientists.

Advantages

Outlearn your competition

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Learn 10x faster

Quantify requires 60-90% less spend (or time/observations) to get conclusive results in comparison to significance testing, so you can test roughly 10x as much for the same budget and ultimately outlearn your competition.

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Meaningful conclusions

As soon as the results are credible enough, Quantify automatically draws meaningful conclusions. In comparison to significance testing, Quantify provides conclusions up to 10x faster.

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Instant insights, always accurate

No more waiting for results: You can peek into your results at any given time to spot early issues or trends. Switch out or remove poor-performing variations during the tests, while results are guaranteed to be always accurate, allowing for a highly accelerated experimentation and optimization cycle.

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Test many variations at once

Quantify's highly efficient A/B/n testing capabilities allow you to test any number of variations simultaneously, which provides additional flexibility and speed.

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Challenge everything

Test and compare any aspect of your marketing activities, even across channels: For example, campaigns, targeting settings, media strategies, creative designs, email copy, product ideas, audiences, media platforms, RTB domains, and many more.

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Avoid bad decisions

Quantify helps you to avoid making bad decisions based on average numbers: By leveraging superior Bayesian statistics, you can make well-founded decisions at a vastly higher pace.

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No data scientists required

Quantify is built for marketers and does not require a background in data science or statistics. Quantify makes Bayesian statistics easily accessible by providing credible intervals that specify 95% of all probable values.

QuantifyNow Live-Demo

Have a look at QuantifyNow. In this demo you will find an example-report based on dummy data:

Launch Demo

"When you’re doing concrete A/B tests, you have to run them for two to three weeks at a time, and then you have to wait, which is what we did before. Now we’re just moving much more quickly."

– Sadie Daryan, Head of Global Display and App Marketing

"Being able to test my designs and understand how actual consumers perceive them became a crucial part of my design process."

– Kai Linke, Award-Winning Product Designer

Data sources

Integrations

Compared to the enterprise version of Quantify, which has a comprehensive range of features and integrations, QuantifyNow will only provide a limited set of the most popular platform integrations.

As of launch, two common data integrations are available. We are currently in the process of adding more integrations. Do let us know your preferred integration in this survey.

Details

Frequently asked questions

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What is QuantifyNow?

QuantifyNow is a free but feature-limited version of our flagship product Quantify, a statistics and experimentation engine that processes and analyses arbitrary marketing data from various sources to generate actionable insights. With QuantifyNow you can create one-time reports that are limited to a maximum of 3 weeks of data.

Comprehensive premium features like notifications, alerts, automatic data updates, and much more are available in the premium version of Quantify.

This video explains how Quantify works and why you need it: Watch Video

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Why should I test and experiment?

We strongly believe that an experimentation-focused approach is the key to sustain a successful marketing organization. Marketing teams that master testing and learning can outlearn their competition by delivering not only better products, but also more relevant ads and experiences, and ultimately drive growth. Transforming your team to have an experimental mindset and building a culture of experimentation is the first step.

If you are interested in more details about building a culture of experimentation, check out this article:
How to Establish a Data-Driven Culture of Experimentation and Supercharge Growth

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What is always-on experimentation?

Always-on experimentation is an agile process that enables companies to iterate, optimize, and innovate faster by continually learning from ever-running marketing experiments. Implementing always-on experimentation can have a tremendous impact on how businesses conduct their marketing practices and drive growth.

Learn more about how agile marketing teams leverage always-on experimentation in this article:
How Winning Teams Leverage Always-on Experimentation

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Does QuantifyNow only work for marketing experiments?

No, even if you currently do not run marketing experiments, you can still leverage Quantify to make better data-driven decisions.

Quantify will help you to understand the credibility of your performance data and enable you to make better decisions faster, while eliminating guesswork and the dependency on data scientists.

With all the possibilities that Quantify provides, there are still common-sense limits. For instance, it might not make sense to compare click-through rates of large advertisements with those of text links. While Quantify will deliver results in this case, the usefulness of these results will be limited.

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How are Bayesian statistics superior to average numbers?

Bayesian statistics solve the intrinsic problem of making decisions based on average numbers: The impossibility to know the correct point in time to stop a test.

Due to this timing problem, tests are either stopped too early, which leads to bad decisions as the results are not yet representative – or the tests are stopped too late, which constitutes a waste of time and marketing spend, especially with regard to poor performing variations.

Due to its probabilistic approach, Bayesian statistics deliver sound results at any point in time.

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How are Bayesian statistics superior to A/B significance testing?

A/B significance testing comes with a number of drawbacks that can be avoided with Bayesian statistics:

Complicated to setup
No test peeking
Data hungry
Limited amount of variations in each test
Results are abstract and indirect
Dependencies on expert resources

Learn in this article why you should move away from A/B significance testing:
7 Reasons to Move Away From A/B Significance Testing

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How does Quantify work specifically?

Quantify uses Bayesian statistics, which is in many ways superior to other approaches. Quantify models probability distributions for all metrics and calculates credible intervals. A credible interval specifies the range that includes 95% of all probable values. Quantify also provides credible intervals for actual lifts, so it can tell you how much better a variation performs in comparison to other variations. These tests can be run both as on-off tests or as continuous tests, even while changing the test’s underlying variations during the process.

As soon as the results are credible enough, Quantify will automatically draw meaningful conclusions and highlight winners and losers.

We explain the the basics of Bayesian statistics in this video: Watch Video

If you are interested in the mathematical theory and methodology behind our Bayesian statistics engine, please check-out our whitepaper: Download Whitepaper

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What are potential use-cases?

We continuously learn about new use-cases for Quantify as you can leverage it to analyze any test or outcome that is somewhat measurable. Here are a couple of examples:

User Acquisition, Dynamic Creative Optimization, RTB Optimization, Campaign Optimisation, Channel Exploration, Business Model Validation, Incrementality Testing, Onsite Experiments, Innovation Testing, Conversion/Funnel Optimization, Product/Feature Validation, Promotion/Coupon/Price Testing, CRM/Email Optimization, Survey Analytics, Audience Optimization, POS Optimization, Direct Mail Testing

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What metrics are available?

We can model every conceivable metric in Quantify. Quantify supports the following metric types:

Binary metrics (e.g. conversion rate)
Time based metrics (e.g. engagement time)
Value/attribution chained metrics (e.g. return on investment or advertising spend)
Monetary metrics (e.g. cost per conversion)
User or cohort based metrics (e.g. customer lifetime value)
Predictive metrics (e.g. predicted customer lifetime value or return on investment)

The free version of Quantify, QuantifyNow, features a limited set of metrics for each data source. Please reach out via the feedback form if you are missing a metric.

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What if I don’t find my data source?

QuantifyNow provides a growing number of integrations with selected media partners. If your data source is not available at this time, you can upload your data via an Excel or CSV file (only conversion rates, though).

We are currently working on adding new data sources. If you are missing a data source, please let us know your priorities via this survey.

You might also consider upgrading to the full version of Quantify that provides the ability to integrate any data source and metric.

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Superior statistics for 10x faster decision making in marketing analytics